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https://github.com/NicolasBohn/NexQuant.git
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feat: a unified CoSTEER to fit more scenarios (#491)
* Use ExtendedBaseSettings to replace BaseSettings * update a more general way to pass the default setting * update all code * fix CI * fix CI * fix qlib scenario * fix CI * fix CI * fix CI & add data science interfaces * remove redundant code * abandon costeer knowledge base v1 --------- Co-authored-by: Xu Yang <xuyang1@microsoft.com> Co-authored-by: XianBW <36835909+XianBW@users.noreply.github.com>
This commit is contained in:
Xu Yang
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from __future__ import annotations
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import json
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from pathlib import Path
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from jinja2 import Environment, StrictUndefined
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from rdagent.components.coder.CoSTEER.evolving_strategy import (
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MultiProcessEvolvingStrategy,
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)
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from rdagent.components.coder.CoSTEER.knowledge_management import (
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CoSTEERQueriedKnowledge,
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CoSTEERQueriedKnowledgeV2,
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)
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from rdagent.components.coder.factor_coder.config import FACTOR_COSTEER_SETTINGS
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from rdagent.components.coder.factor_coder.factor import FactorFBWorkspace, FactorTask
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from rdagent.core.prompts import Prompts
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from rdagent.oai.llm_conf import LLM_SETTINGS
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from rdagent.oai.llm_utils import APIBackend
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implement_prompts = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
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class FactorMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
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def __init__(self, *args, **kwargs) -> None:
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super().__init__(*args, **kwargs)
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self.num_loop = 0
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self.haveSelected = False
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def error_summary(
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self,
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target_task: FactorTask,
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queried_former_failed_knowledge_to_render: list,
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queried_similar_error_knowledge_to_render: list,
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) -> str:
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error_summary_system_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(implement_prompts["evolving_strategy_error_summary_v2_system"])
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.render(
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scenario=self.scen.get_scenario_all_desc(target_task),
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factor_information_str=target_task.get_task_information(),
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code_and_feedback=queried_former_failed_knowledge_to_render[-1].get_implementation_and_feedback_str(),
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)
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.strip("\n")
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)
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for _ in range(10): # max attempt to reduce the length of error_summary_user_prompt
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error_summary_user_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(implement_prompts["evolving_strategy_error_summary_v2_user"])
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.render(
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queried_similar_error_knowledge=queried_similar_error_knowledge_to_render,
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)
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.strip("\n")
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)
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if (
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APIBackend().build_messages_and_calculate_token(
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user_prompt=error_summary_user_prompt, system_prompt=error_summary_system_prompt
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)
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< LLM_SETTINGS.chat_token_limit
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):
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break
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elif len(queried_similar_error_knowledge_to_render) > 0:
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queried_similar_error_knowledge_to_render = queried_similar_error_knowledge_to_render[:-1]
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error_summary_critics = APIBackend(
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use_chat_cache=FACTOR_COSTEER_SETTINGS.coder_use_cache
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).build_messages_and_create_chat_completion(
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user_prompt=error_summary_user_prompt, system_prompt=error_summary_system_prompt, json_mode=False
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)
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return error_summary_critics
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def implement_one_task(
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self,
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target_task: FactorTask,
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queried_knowledge: CoSTEERQueriedKnowledge,
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) -> str:
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target_factor_task_information = target_task.get_task_information()
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queried_similar_successful_knowledge = (
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queried_knowledge.task_to_similar_task_successful_knowledge[target_factor_task_information]
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if queried_knowledge is not None
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else []
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) # A list, [success task implement knowledge]
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if isinstance(queried_knowledge, CoSTEERQueriedKnowledgeV2):
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queried_similar_error_knowledge = (
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queried_knowledge.task_to_similar_error_successful_knowledge[target_factor_task_information]
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if queried_knowledge is not None
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else {}
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) # A dict, {{error_type:[[error_imp_knowledge, success_imp_knowledge],...]},...}
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else:
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queried_similar_error_knowledge = {}
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queried_former_failed_knowledge = (
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queried_knowledge.task_to_former_failed_traces[target_factor_task_information][0]
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if queried_knowledge is not None
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else []
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)
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queried_former_failed_knowledge_to_render = queried_former_failed_knowledge
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latest_attempt_to_latest_successful_execution = queried_knowledge.task_to_former_failed_traces[
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target_factor_task_information
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][1]
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system_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(
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implement_prompts["evolving_strategy_factor_implementation_v1_system"],
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)
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.render(
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scenario=self.scen.get_scenario_all_desc(target_task, filtered_tag="feature"),
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queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
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)
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)
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queried_similar_successful_knowledge_to_render = queried_similar_successful_knowledge
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queried_similar_error_knowledge_to_render = queried_similar_error_knowledge
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# 动态地防止prompt超长
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for _ in range(10): # max attempt to reduce the length of user_prompt
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# 总结error（可选）
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if (
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isinstance(queried_knowledge, CoSTEERQueriedKnowledgeV2)
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and FACTOR_COSTEER_SETTINGS.v2_error_summary
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and len(queried_similar_error_knowledge_to_render) != 0
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and len(queried_former_failed_knowledge_to_render) != 0
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):
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error_summary_critics = self.error_summary(
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target_task,
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queried_former_failed_knowledge_to_render,
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queried_similar_error_knowledge_to_render,
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)
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else:
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error_summary_critics = None
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# 构建user_prompt。开始写代码
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user_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(
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implement_prompts["evolving_strategy_factor_implementation_v2_user"],
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)
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.render(
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factor_information_str=target_factor_task_information,
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queried_similar_successful_knowledge=queried_similar_successful_knowledge_to_render,
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queried_similar_error_knowledge=queried_similar_error_knowledge_to_render,
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error_summary_critics=error_summary_critics,
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latest_attempt_to_latest_successful_execution=latest_attempt_to_latest_successful_execution,
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)
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.strip("\n")
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)
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if (
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APIBackend().build_messages_and_calculate_token(user_prompt=user_prompt, system_prompt=system_prompt)
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< LLM_SETTINGS.chat_token_limit
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):
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break
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elif len(queried_former_failed_knowledge_to_render) > 1:
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queried_former_failed_knowledge_to_render = queried_former_failed_knowledge_to_render[1:]
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elif len(queried_similar_successful_knowledge_to_render) > len(
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queried_similar_error_knowledge_to_render,
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):
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queried_similar_successful_knowledge_to_render = queried_similar_successful_knowledge_to_render[:-1]
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elif len(queried_similar_error_knowledge_to_render) > 0:
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queried_similar_error_knowledge_to_render = queried_similar_error_knowledge_to_render[:-1]
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code = json.loads(
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APIBackend(
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use_chat_cache=FACTOR_COSTEER_SETTINGS.coder_use_cache
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).build_messages_and_create_chat_completion(
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user_prompt=user_prompt, system_prompt=system_prompt, json_mode=True
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)
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)["code"]
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return code
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def assign_code_list_to_evo(self, code_list, evo):
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for index in range(len(evo.sub_tasks)):
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if code_list[index] is None:
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continue
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if evo.sub_workspace_list[index] is None:
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evo.sub_workspace_list[index] = FactorFBWorkspace(target_task=evo.sub_tasks[index])
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evo.sub_workspace_list[index].inject_code(**{"factor.py": code_list[index]})
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return evo
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